code: def create_run_config(
      cpu_cluster,
      docker_proc_type,
      add_gpu_dependencies,
      add_spark_dependencies,
      conda_pkg_jdk,
      conda_pkg_python,
      commit_sha,
  ):
      """
      AzureML requires the run environment to be setup prior to submission.
      This configures a docker persistent compute.  Even though
      it is called Persistent compute, AzureML handles startup/shutdown
      of the compute environment.
  
      Args:
              cpu_cluster      (str)          : Names the cluster for the test
                                                  In the case of unit tests, any of
                                                  the following:
                                                  - Reco_cpu_test
                                                  - Reco_gpu_test
              docker_proc_type (str)          : processor type, cpu or gpu
              add_gpu_dependencies (bool)     : True if gpu packages should be
                                          added to the conda environment, else False
              add_spark_dependencies (bool)   : True if PySpark packages should be
                                          added to the conda environment, else False
              commit_sha (str)                : the commit that triggers the workflow
  
      Return:
            run_azuremlcompute : AzureML run config
      """
  
      run_azuremlcompute = RunConfiguration()
      run_azuremlcompute.target = cpu_cluster
      run_azuremlcompute.environment.docker.enabled = True
      run_azuremlcompute.environment.docker.base_image = (
          docker_proc_type
      )
  
      # Use conda_dependencies.yml to create a conda environment in
      # the Docker image for execution
      # False means the user will provide a conda file for setup
      # True means the user will manually configure the environment
      run_azuremlcompute.environment.python.user_managed_dependencies = (
          False
      )
  
      conda_dep = CondaDependencies()
      conda_dep.add_conda_package(conda_pkg_python)
      conda_dep.add_pip_package(
          "pymanopt@https://github.com/pymanopt/pymanopt/archive/fb36a272cdeecb21992cfd9271eb82baafeb316d.zip"
      )
  
      # install recommenders
      reco_extras = "dev"
      if add_gpu_dependencies and add_spark_dependencies:
          conda_dep.add_channel("conda-forge")
          conda_dep.add_conda_package(conda_pkg_jdk)
          reco_extras = reco_extras + ",spark,gpu"
      elif add_gpu_dependencies:
          reco_extras = reco_extras + ",gpu"
      elif add_spark_dependencies:
          conda_dep.add_channel("conda-forge")
          conda_dep.add_conda_package(conda_pkg_jdk)
          reco_extras = reco_extras + ",spark"
  
      conda_dep.add_pip_package(
          f"recommenders[{reco_extras}]@git+https://github.com/recommenders-team/recommenders.git@{commit_sha}"
      )
  
      run_azuremlcompute.environment.python.conda_dependencies = (
          conda_dep
      )
      return run_azuremlcompute
  
file_path: recommenders/tests/ci/azureml_tests/submit_groupwise_azureml_pytest.py
imports: import argparse
  import logging
  import glob
  from azureml.core.authentication import AzureCliAuthentication
  from azureml.core import Workspace
  from azureml.core import Experiment
  from azureml.core.runconfig import RunConfiguration
  from azureml.core.conda_dependencies import CondaDependencies
  from azureml.core.script_run_config import ScriptRunConfig
  from azureml.core.compute import ComputeTarget, AmlCompute
  from azureml.core.compute_target import ComputeTargetException
  from azureml.core.workspace import WorkspaceException
label_desc: Use image node_id 3 for calling a global function with example usage: create_run_config(cpu_cluster, docker_proc_type, add_gpu_dependencies, add_spark_dependencies, conda_pkg_jdk, conda_pkg_python, commit_sha) and returns: run_azuremlcompute